python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 20 -a ' -a 3318 ' -s datou_3318 -M 0 -S 0 -U 95,95,120 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/caffe_cuda8_python3/python', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 475589 load datou : 3318 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? [(photo_id, hashtag_id, hashtag_type, x0, x1, y0, y1, score, seg_temp, polygons), ...] was removed should we ? chemin de la photo was removed should we ? [ (photo_id_loc, hashtag_id, hashtag_type, x0, x1, y0, y1, score, None), ...] was removed should we ? chemin de la photo was removed should we ? id de la photo (peut être local ou global) was removed should we ? chemin de la photo was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? donnée sous forme de texte was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? id de la photo (peut être local ou global) was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? None was removed should we ? donnée sous forme de nombre was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? donnée sous forme de texte was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) load thcls load THCL from format json or kwargs add thcl : 2847 in CacheModelConfig load pdts add pdt : 5275 in CacheModelConfig Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 3318, datou_cur_ids : ['4341986'] with mtr_portfolio_ids : ['30348404'] and first list_photo_ids : [] new path : /proc/475589/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 7 ; length of list_pids : 7 ; length of list_args : 7 time to download the photos : 1.0193564891815186 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Mon Feb 9 14:10:29 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3000 l 3632 free memory gpu now : 1997 wait 20 seconds l 3637 free memory gpu now : 1997 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-02-09 14:10:52.555931: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-02-09 14:10:52.586427: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-02-09 14:10:52.588306: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fabec000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-02-09 14:10:52.588364: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-02-09 14:10:52.592080: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-02-09 14:10:52.718929: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x161c93b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-02-09 14:10:52.718985: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-02-09 14:10:52.719875: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2026-02-09 14:10:52.720331: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-02-09 14:10:52.722508: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-02-09 14:10:52.725280: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-02-09 14:10:52.725611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-02-09 14:10:52.731063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-02-09 14:10:52.732896: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-02-09 14:10:52.741708: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-02-09 14:10:52.742703: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-02-09 14:10:52.742784: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-02-09 14:10:52.743296: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-02-09 14:10:52.743312: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-02-09 14:10:52.743332: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-02-09 14:10:52.744306: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1620 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2026-02-09 14:10:53.309206: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2026-02-09 14:10:53.309383: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-02-09 14:10:53.309414: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-02-09 14:10:53.309429: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-02-09 14:10:53.309443: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-02-09 14:10:53.309462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-02-09 14:10:53.309477: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-02-09 14:10:53.309492: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-02-09 14:10:53.310337: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-02-09 14:10:53.311641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2026-02-09 14:10:53.311688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-02-09 14:10:53.311710: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-02-09 14:10:53.311735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-02-09 14:10:53.311758: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-02-09 14:10:53.311779: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-02-09 14:10:53.311802: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-02-09 14:10:53.311831: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-02-09 14:10:53.312658: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-02-09 14:10:53.312713: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-02-09 14:10:53.312725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-02-09 14:10:53.312735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-02-09 14:10:53.313624: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1620 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2026-02-09 14:11:01.210443: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-02-09 14:11:01.483906: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-02-09 14:11:03.385183: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.385305: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.392273: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.392322: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.444261: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.444317: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.486638: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.486684: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.540525: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.540586: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-02-09 14:11:03.542345: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature. 2026-02-09 14:11:03.559755: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.560720: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.591645: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.592347: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.594262: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.594907: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.603727: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.604334: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.610512: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.611119: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.612800: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.613395: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.642259: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.642874: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.643412: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.643950: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.647640: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.648192: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.666484: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.667085: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.667668: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.668252: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.681716: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.682363: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.682917: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.683462: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.688280: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.688878: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.693842: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.694503: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.707349: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.707945: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.712341: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.712905: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.713698: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.714229: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.724889: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.725447: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.725987: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.726542: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.727068: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.727585: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.800129: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.800666: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.808402: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.808946: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.834093: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.834825: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.835499: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.836146: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.840546: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.841256: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.841916: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.842625: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.844512: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.844559: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2026-02-09 14:11:03.854164: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.854782: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.864090: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.864704: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.865283: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.865855: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.866458: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-02-09 14:11:03.867106: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.08G (1163264000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 27 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 27 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 22 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 17 Detection mask done ! Trying to reset tf kernel 476003 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 87 tf kernel not reseted sub process len(results) : 7 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 7 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1280 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] DEBUG bbox = [339, 1428, 537, 1668] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0006496906280517578 nb_pixel_total : 13145 time to create 1 rle with old method : 0.015680789947509766 length of segment : 187 DEBUG bbox = [933, 702, 1020, 846] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00016736984252929688 nb_pixel_total : 5112 time to create 1 rle with old method : 0.006133556365966797 length of segment : 72 DEBUG bbox = [585, 1671, 666, 1797] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.000171661376953125 nb_pixel_total : 7149 time to create 1 rle with old method : 0.008478641510009766 length of segment : 81 DEBUG bbox = [960, 927, 1032, 999] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00010848045349121094 nb_pixel_total : 2812 time to create 1 rle with old method : 0.003448963165283203 length of segment : 68 DEBUG bbox = [705, 1038, 1080, 1905] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0038557052612304688 nb_pixel_total : 167411 time to create 1 rle with new method : 0.010152101516723633 length of segment : 329 DEBUG bbox = [0, 0, 1080, 1608] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.3392021656036377 nb_pixel_total : 523132 time to create 1 rle with new method : 0.23173093795776367 length of segment : 1338 DEBUG bbox = [576, 1677, 663, 1782] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00015926361083984375 nb_pixel_total : 7182 time to create 1 rle with old method : 0.008065938949584961 length of segment : 86 DEBUG bbox = [819, 1020, 945, 1203] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00021696090698242188 nb_pixel_total : 11815 time to create 1 rle with old method : 0.013719558715820312 length of segment : 133 DEBUG bbox = [345, 1695, 456, 1824] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00014925003051757812 nb_pixel_total : 6210 time to create 1 rle with old method : 0.007078409194946289 length of segment : 146 DEBUG bbox = [564, 990, 651, 1098] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00012421607971191406 nb_pixel_total : 5672 time to create 1 rle with old method : 0.006772756576538086 length of segment : 74 DEBUG bbox = [846, 834, 1011, 1068] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00042057037353515625 nb_pixel_total : 21093 time to create 1 rle with old method : 0.02384328842163086 length of segment : 163 DEBUG bbox = [936, 711, 978, 756] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 1085 time to create 1 rle with old method : 0.0014073848724365234 length of segment : 38 DEBUG bbox = [3, 1719, 54, 1818] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 3322 time to create 1 rle with old method : 0.004042148590087891 length of segment : 57 DEBUG bbox = [801, 1212, 906, 1326] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001461505889892578 nb_pixel_total : 7531 time to create 1 rle with old method : 0.008292436599731445 length of segment : 100 DEBUG bbox = [849, 1050, 969, 1179] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00016760826110839844 nb_pixel_total : 8545 time to create 1 rle with old method : 0.009912729263305664 length of segment : 127 DEBUG bbox = [453, 1662, 648, 1893] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004029273986816406 nb_pixel_total : 19713 time to create 1 rle with old method : 0.022069931030273438 length of segment : 335 DEBUG bbox = [447, 1329, 528, 1386] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 2734 time to create 1 rle with old method : 0.003284454345703125 length of segment : 78 DEBUG bbox = [405, 1767, 474, 1851] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 9.751319885253906e-05 nb_pixel_total : 3577 time to create 1 rle with old method : 0.0043489933013916016 length of segment : 60 DEBUG bbox = [1008, 399, 1074, 474] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 7.605552673339844e-05 nb_pixel_total : 2567 time to create 1 rle with old method : 0.0030832290649414062 length of segment : 63 DEBUG bbox = [789, 1125, 855, 1212] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 3392 time to create 1 rle with old method : 0.003952741622924805 length of segment : 60 DEBUG bbox = [816, 1335, 873, 1413] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 2492 time to create 1 rle with old method : 0.0030596256256103516 length of segment : 54 DEBUG bbox = [6, 1746, 72, 1860] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 9.703636169433594e-05 nb_pixel_total : 4847 time to create 1 rle with old method : 0.005837440490722656 length of segment : 64 DEBUG bbox = [969, 339, 1044, 444] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00010156631469726562 nb_pixel_total : 3406 time to create 1 rle with old method : 0.004136562347412109 length of segment : 59 DEBUG bbox = [738, 1437, 825, 1569] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00014472007751464844 nb_pixel_total : 7703 time to create 1 rle with old method : 0.008991241455078125 length of segment : 81 DEBUG bbox = [567, 1428, 699, 1515] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00015282630920410156 nb_pixel_total : 6217 time to create 1 rle with old method : 0.0068891048431396484 length of segment : 137 DEBUG bbox = [1020, 396, 1074, 504] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 8.678436279296875e-05 nb_pixel_total : 2910 time to create 1 rle with old method : 0.0035593509674072266 length of segment : 52 DEBUG bbox = [645, 1422, 795, 1662] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003228187561035156 nb_pixel_total : 13548 time to create 1 rle with old method : 0.015468835830688477 length of segment : 181 DEBUG bbox = [378, 1515, 483, 1641] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00016951560974121094 nb_pixel_total : 8789 time to create 1 rle with old method : 0.010196208953857422 length of segment : 141 DEBUG bbox = [759, 1155, 819, 1239] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 2292 time to create 1 rle with old method : 0.002753019332885742 length of segment : 51 DEBUG bbox = [612, 1443, 711, 1599] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00017333030700683594 nb_pixel_total : 8479 time to create 1 rle with old method : 0.009706735610961914 length of segment : 88 DEBUG bbox = [714, 1356, 822, 1524] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00018310546875 nb_pixel_total : 7524 time to create 1 rle with old method : 0.008685111999511719 length of segment : 81 DEBUG bbox = [747, 648, 849, 753] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00013446807861328125 nb_pixel_total : 5655 time to create 1 rle with old method : 0.006585121154785156 length of segment : 99 DEBUG bbox = [714, 1365, 810, 1527] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00014853477478027344 nb_pixel_total : 7490 time to create 1 rle with old method : 0.008820533752441406 length of segment : 78 DEBUG bbox = [801, 837, 861, 918] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 2427 time to create 1 rle with old method : 0.0029113292694091797 length of segment : 52 DEBUG bbox = [852, 648, 1041, 936] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003707408905029297 nb_pixel_total : 7055 time to create 1 rle with old method : 0.00866079330444336 length of segment : 253 DEBUG bbox = [408, 1824, 567, 1920] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00047779083251953125 nb_pixel_total : 8663 time to create 1 rle with old method : 0.00994110107421875 length of segment : 144 DEBUG bbox = [744, 639, 858, 750] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00033545494079589844 nb_pixel_total : 5736 time to create 1 rle with old method : 0.006787538528442383 length of segment : 100 DEBUG bbox = [867, 432, 951, 543] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002586841583251953 nb_pixel_total : 5162 time to create 1 rle with old method : 0.006063938140869141 length of segment : 78 DEBUG bbox = [420, 1236, 495, 1329] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001842975616455078 nb_pixel_total : 4129 time to create 1 rle with old method : 0.004781246185302734 length of segment : 76 DEBUG bbox = [336, 1749, 459, 1839] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002942085266113281 nb_pixel_total : 6354 time to create 1 rle with old method : 0.0072193145751953125 length of segment : 114 DEBUG bbox = [291, 1359, 354, 1449] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00017523765563964844 nb_pixel_total : 4117 time to create 1 rle with old method : 0.00476384162902832 length of segment : 56 DEBUG bbox = [348, 1533, 420, 1617] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00014328956604003906 nb_pixel_total : 3377 time to create 1 rle with old method : 0.003987550735473633 length of segment : 65 DEBUG bbox = [726, 1011, 825, 1116] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002455711364746094 nb_pixel_total : 3810 time to create 1 rle with old method : 0.00455784797668457 length of segment : 88 DEBUG bbox = [528, 1257, 855, 1830] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.002251863479614258 nb_pixel_total : 43879 time to create 1 rle with old method : 0.04825329780578613 length of segment : 387 DEBUG bbox = [885, 798, 987, 954] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003228187561035156 nb_pixel_total : 2190 time to create 1 rle with old method : 0.002756834030151367 length of segment : 66 DEBUG bbox = [0, 24, 1080, 1485] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.017115116119384766 nb_pixel_total : 488274 time to create 1 rle with new method : 0.21236157417297363 length of segment : 1285 DEBUG bbox = [813, 813, 1044, 1026] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0008108615875244141 nb_pixel_total : 20216 time to create 1 rle with old method : 0.022249460220336914 length of segment : 240 DEBUG bbox = [597, 1110, 675, 1164] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001742839813232422 nb_pixel_total : 2342 time to create 1 rle with old method : 0.002691984176635742 length of segment : 70 DEBUG bbox = [831, 489, 1074, 1098] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0015251636505126953 nb_pixel_total : 81961 time to create 1 rle with old method : 0.09359955787658691 length of segment : 305 DEBUG bbox = [669, 1626, 759, 1779] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002944469451904297 nb_pixel_total : 7838 time to create 1 rle with old method : 0.008514642715454102 length of segment : 81 DEBUG bbox = [795, 1110, 915, 1281] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003299713134765625 nb_pixel_total : 12639 time to create 1 rle with old method : 0.013971328735351562 length of segment : 121 DEBUG bbox = [840, 726, 921, 792] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 2439 time to create 1 rle with old method : 0.002836465835571289 length of segment : 66 DEBUG bbox = [504, 1431, 609, 1524] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00026535987854003906 nb_pixel_total : 6512 time to create 1 rle with old method : 0.007287502288818359 length of segment : 102 DEBUG bbox = [759, 1374, 840, 1548] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00023674964904785156 nb_pixel_total : 6323 time to create 1 rle with old method : 0.007361650466918945 length of segment : 65 DEBUG bbox = [711, 774, 780, 888] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00017905235290527344 nb_pixel_total : 3941 time to create 1 rle with old method : 0.004623889923095703 length of segment : 68 DEBUG bbox = [381, 1728, 480, 1857] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00028133392333984375 nb_pixel_total : 8145 time to create 1 rle with old method : 0.008999824523925781 length of segment : 96 DEBUG bbox = [819, 891, 993, 1023] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.000518798828125 nb_pixel_total : 12148 time to create 1 rle with old method : 0.013553619384765625 length of segment : 185 DEBUG bbox = [105, 0, 1080, 882] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.009310245513916016 nb_pixel_total : 55293 time to create 1 rle with old method : 0.06137228012084961 length of segment : 659 DEBUG bbox = [606, 771, 690, 831] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 9.560585021972656e-05 nb_pixel_total : 2897 time to create 1 rle with old method : 0.0033261775970458984 length of segment : 78 DEBUG bbox = [819, 1392, 858, 1512] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00012540817260742188 nb_pixel_total : 1409 time to create 1 rle with old method : 0.0019278526306152344 length of segment : 31 DEBUG bbox = [1005, 549, 1080, 657] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.000110626220703125 nb_pixel_total : 5876 time to create 1 rle with old method : 0.006523609161376953 length of segment : 73 DEBUG bbox = [567, 12, 1080, 351] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0013422966003417969 nb_pixel_total : 76093 time to create 1 rle with old method : 0.08841371536254883 length of segment : 494 DEBUG bbox = [909, 666, 984, 744] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00014495849609375 nb_pixel_total : 3814 time to create 1 rle with old method : 0.004653215408325195 length of segment : 70 DEBUG bbox = [909, 1023, 951, 1128] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00011086463928222656 nb_pixel_total : 2509 time to create 1 rle with old method : 0.0031778812408447266 length of segment : 38 DEBUG bbox = [684, 1371, 801, 1554] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004248619079589844 nb_pixel_total : 12269 time to create 1 rle with old method : 0.014166116714477539 length of segment : 107 DEBUG bbox = [708, 936, 795, 1035] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00024247169494628906 nb_pixel_total : 5329 time to create 1 rle with old method : 0.006049394607543945 length of segment : 79 DEBUG bbox = [531, 1566, 618, 1644] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00023412704467773438 nb_pixel_total : 3288 time to create 1 rle with old method : 0.003991127014160156 length of segment : 74 DEBUG bbox = [879, 1113, 957, 1233] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00020813941955566406 nb_pixel_total : 4143 time to create 1 rle with old method : 0.004603385925292969 length of segment : 68 DEBUG bbox = [993, 516, 1071, 585] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001709461212158203 nb_pixel_total : 2615 time to create 1 rle with old method : 0.003113269805908203 length of segment : 64 DEBUG bbox = [762, 954, 963, 1161] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0005295276641845703 nb_pixel_total : 18989 time to create 1 rle with old method : 0.021473169326782227 length of segment : 229 DEBUG bbox = [822, 777, 909, 891] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001842975616455078 nb_pixel_total : 6883 time to create 1 rle with old method : 0.007933378219604492 length of segment : 92 DEBUG bbox = [411, 1854, 462, 1920] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00012803077697753906 nb_pixel_total : 1954 time to create 1 rle with old method : 0.0024001598358154297 length of segment : 41 DEBUG bbox = [519, 1686, 747, 1911] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0007827281951904297 nb_pixel_total : 30651 time to create 1 rle with old method : 0.03526616096496582 length of segment : 241 time spent for convertir_results : 4.719667673110962 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 73 chid ids of type : 3594 Number RLEs to save : 11362 save missing photos in datou_result : time spend for datou_step_exec : 49.74949550628662 time spend to save output : 0.794823408126831 total time spend for step 1 : 50.54431891441345 step2:crop_condition Mon Feb 9 14:11:19 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 7 ! batch 1 Loaded 73 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 30 About to insert : list_path_to_insert length 30 new photo from crops ! About to upload 30 photos upload in portfolio : 3736932 init cache_photo without model_param we have 30 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1770642682_475589 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847174_0.png', 0, 238, 185, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847180_0.png', 0, 105, 86, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847182_0.png', 0, 114, 94, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847189_0.png', 0, 221, 186, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847191_0.png', 0, 82, 59, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847193_0.png', 0, 83, 60, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847195_0.png', 0, 114, 61, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847197_0.png', 0, 130, 81, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847198_0.png', 0, 78, 131, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847201_0.png', 0, 124, 100, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847203_0.png', 0, 149, 87, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847205_0.png', 0, 98, 99, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847206_0.png', 0, 144, 72, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847208_0.png', 0, 216, 156, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847209_0.png', 0, 93, 144, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847212_0.png', 0, 87, 72, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847213_0.png', 0, 88, 114, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847214_0.png', 0, 84, 55, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847220_0.png', 0, 209, 229, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847221_0.png', 0, 50, 70, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847222_0.png', 0, 535, 225, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847223_0.png', 0, 151, 81, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847224_0.png', 0, 170, 117, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847225_0.png', 0, 60, 65, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847232_0.png', 0, 57, 78, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847235_0.png', 0, 308, 457, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847240_0.png', 0, 77, 73, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847242_0.png', 0, 65, 56, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847245_0.png', 0, 59, 41, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642687), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847246_0.png', 0, 224, 201, 0, 1770642687,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 30 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.073522329330444 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 33 About to insert : list_path_to_insert length 33 new photo from crops ! About to upload 33 photos upload in portfolio : 3736932 init cache_photo without model_param we have 33 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1770642690_475589 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847176_0.png', 0, 119, 81, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847181_0.png', 0, 163, 111, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847183_0.png', 0, 102, 71, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847184_0.png', 0, 188, 151, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847185_0.png', 0, 42, 38, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847186_0.png', 0, 96, 49, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847187_0.png', 0, 105, 100, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847188_0.png', 0, 115, 106, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847192_0.png', 0, 66, 63, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847194_0.png', 0, 77, 49, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847196_0.png', 0, 93, 59, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847199_0.png', 0, 92, 52, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847200_0.png', 0, 203, 147, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847204_0.png', 0, 144, 76, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847210_0.png', 0, 99, 100, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847211_0.png', 0, 97, 77, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847215_0.png', 0, 79, 65, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847216_0.png', 0, 77, 88, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847217_0.png', 0, 521, 246, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847218_0.png', 0, 122, 66, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847226_0.png', 0, 93, 102, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847227_0.png', 0, 145, 65, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847228_0.png', 0, 106, 68, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847229_0.png', 0, 112, 96, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847230_0.png', 0, 120, 169, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847233_0.png', 0, 102, 31, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847236_0.png', 0, 75, 69, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847237_0.png', 0, 94, 38, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847238_0.png', 0, 165, 104, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847239_0.png', 0, 93, 79, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847241_0.png', 0, 95, 68, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847243_0.png', 0, 196, 157, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642697), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847244_0.png', 0, 114, 81, 0, 1770642697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 33 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.467746019363403 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1770642699_475589 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642700), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847190_0.png', 0, 49, 78, 0, 1770642700,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642700), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847202_0.png', 0, 68, 51, 0, 1770642700,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642700), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847234_0.png', 0, 107, 70, 0, 1770642700,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.2394893169403076 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1770642705_475589 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642706), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847178_0.png', 0, 814, 319, 0, 1770642706,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642706), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847179_0.png', 0, 1348, 995, 0, 1770642706,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642706), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847219_0.png', 0, 1303, 978, 0, 1770642706,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642706), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847231_0.png', 0, 768, 801, 0, 1770642706,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.2374036312103271 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1770642707_475589 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642708), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847175_0.png', 0, 129, 71, 0, 1770642708,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642708), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847177_0.png', 0, 61, 67, 0, 1770642708,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1770642708), 0.0, 0.0, 14, '', 0, 0, '1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847207_0.png', 0, 74, 50, 0, 1770642708,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.5023651123046875 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1407288611, 1407287658, 1407287630, 1407287600, 1407287381, 1407287380, 1407287379] Looping around the photos to save general results len do output : 73 /1407310832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407310871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311056Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311057Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311065Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311085Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311088Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311147Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311154Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1407311156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407288611', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287658', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287630', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287600', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287381', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287380', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287379', None, None, None, None, None, '4341986') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 226 time used for this insertion : 0.03831076622009277 save_final save missing photos in datou_result : time spend for datou_step_exec : 29.003652334213257 time spend to save output : 0.04105257987976074 total time spend for step 2 : 29.044704914093018 step3:rle_unique_nms_with_priority Mon Feb 9 14:11:48 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 73 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 8 nb_hashtags : 4 time to prepare the origin masks : 0.9389021396636963 time for calcul the mask position with numpy : 0.1962893009185791 nb_pixel_total : 1346749 time to create 1 rle with new method : 0.22783255577087402 time for calcul the mask position with numpy : 0.010660886764526367 nb_pixel_total : 11815 time to create 1 rle with old method : 0.013596773147583008 time for calcul the mask position with numpy : 0.006734371185302734 nb_pixel_total : 447 time to create 1 rle with old method : 0.0006718635559082031 time for calcul the mask position with numpy : 0.03001546859741211 nb_pixel_total : 518960 time to create 1 rle with new method : 0.20431733131408691 time for calcul the mask position with numpy : 0.007996797561645508 nb_pixel_total : 167411 time to create 1 rle with new method : 0.17430853843688965 time for calcul the mask position with numpy : 0.006601810455322266 nb_pixel_total : 2812 time to create 1 rle with old method : 0.0032923221588134766 time for calcul the mask position with numpy : 0.006779670715332031 nb_pixel_total : 7149 time to create 1 rle with old method : 0.008280277252197266 time for calcul the mask position with numpy : 0.006635427474975586 nb_pixel_total : 5112 time to create 1 rle with old method : 0.005475521087646484 time for calcul the mask position with numpy : 0.006314992904663086 nb_pixel_total : 13145 time to create 1 rle with old method : 0.014414310455322266 create new chi : 0.9563858509063721 time to delete rle : 0.0301516056060791 batch 1 Loaded 17 chid ids of type : 3594 +++++++++++Number RLEs to save : 5502 TO DO : save crop sub photo not yet done ! save time : 0.40771985054016113 nb_obj : 17 nb_hashtags : 3 time to prepare the origin masks : 0.9825112819671631 time for calcul the mask position with numpy : 0.08408713340759277 nb_pixel_total : 1966138 time to create 1 rle with new method : 0.20651555061340332 time for calcul the mask position with numpy : 0.011254549026489258 nb_pixel_total : 6217 time to create 1 rle with old method : 0.011361122131347656 time for calcul the mask position with numpy : 0.012728452682495117 nb_pixel_total : 7703 time to create 1 rle with old method : 0.015689373016357422 time for calcul the mask position with numpy : 0.010074853897094727 nb_pixel_total : 3387 time to create 1 rle with old method : 0.0038499832153320312 time for calcul the mask position with numpy : 0.010173797607421875 nb_pixel_total : 2222 time to create 1 rle with old method : 0.0024673938751220703 time for calcul the mask position with numpy : 0.006232500076293945 nb_pixel_total : 2492 time to create 1 rle with old method : 0.0027887821197509766 time for calcul the mask position with numpy : 0.006537437438964844 nb_pixel_total : 3392 time to create 1 rle with old method : 0.003734588623046875 time for calcul the mask position with numpy : 0.006322622299194336 nb_pixel_total : 2567 time to create 1 rle with old method : 0.0029337406158447266 time for calcul the mask position with numpy : 0.0061588287353515625 nb_pixel_total : 3577 time to create 1 rle with old method : 0.004094839096069336 time for calcul the mask position with numpy : 0.0064241886138916016 nb_pixel_total : 2734 time to create 1 rle with old method : 0.002996206283569336 time for calcul the mask position with numpy : 0.006369829177856445 nb_pixel_total : 19713 time to create 1 rle with old method : 0.021757841110229492 time for calcul the mask position with numpy : 0.006288290023803711 nb_pixel_total : 8545 time to create 1 rle with old method : 0.009083747863769531 time for calcul the mask position with numpy : 0.00604701042175293 nb_pixel_total : 7531 time to create 1 rle with old method : 0.008164405822753906 time for calcul the mask position with numpy : 0.0061457157135009766 nb_pixel_total : 3322 time to create 1 rle with old method : 0.003576040267944336 time for calcul the mask position with numpy : 0.0059814453125 nb_pixel_total : 1085 time to create 1 rle with old method : 0.001155853271484375 time for calcul the mask position with numpy : 0.006362438201904297 nb_pixel_total : 21093 time to create 1 rle with old method : 0.02275681495666504 time for calcul the mask position with numpy : 0.006579160690307617 nb_pixel_total : 5672 time to create 1 rle with old method : 0.00653839111328125 time for calcul the mask position with numpy : 0.005861043930053711 nb_pixel_total : 6210 time to create 1 rle with old method : 0.006636381149291992 create new chi : 0.5561838150024414 time to delete rle : 0.0006740093231201172 batch 1 Loaded 35 chid ids of type : 3594 ++++++++++++++++++++++Number RLEs to save : 4422 TO DO : save crop sub photo not yet done ! save time : 0.33153629302978516 nb_obj : 13 nb_hashtags : 4 time to prepare the origin masks : 0.17392373085021973 time for calcul the mask position with numpy : 0.020783662796020508 nb_pixel_total : 2002501 time to create 1 rle with new method : 0.1448960304260254 time for calcul the mask position with numpy : 0.006139278411865234 nb_pixel_total : 5162 time to create 1 rle with old method : 0.0058746337890625 time for calcul the mask position with numpy : 0.0059697628021240234 nb_pixel_total : 99 time to create 1 rle with old method : 0.0002484321594238281 time for calcul the mask position with numpy : 0.006775379180908203 nb_pixel_total : 8663 time to create 1 rle with old method : 0.009483098983764648 time for calcul the mask position with numpy : 0.005941867828369141 nb_pixel_total : 7055 time to create 1 rle with old method : 0.008078575134277344 time for calcul the mask position with numpy : 0.006097078323364258 nb_pixel_total : 2427 time to create 1 rle with old method : 0.0027666091918945312 time for calcul the mask position with numpy : 0.005784511566162109 nb_pixel_total : 105 time to create 1 rle with old method : 0.0002510547637939453 time for calcul the mask position with numpy : 0.005940437316894531 nb_pixel_total : 5655 time to create 1 rle with old method : 0.006330013275146484 time for calcul the mask position with numpy : 0.0059356689453125 nb_pixel_total : 7470 time to create 1 rle with old method : 0.008170843124389648 time for calcul the mask position with numpy : 0.00595545768737793 nb_pixel_total : 6924 time to create 1 rle with old method : 0.007856369018554688 time for calcul the mask position with numpy : 0.006174802780151367 nb_pixel_total : 2292 time to create 1 rle with old method : 0.002371072769165039 time for calcul the mask position with numpy : 0.006434917449951172 nb_pixel_total : 8789 time to create 1 rle with old method : 0.009503602981567383 time for calcul the mask position with numpy : 0.0057849884033203125 nb_pixel_total : 13548 time to create 1 rle with old method : 0.01503300666809082 time for calcul the mask position with numpy : 0.006006002426147461 nb_pixel_total : 2910 time to create 1 rle with old method : 0.003209829330444336 create new chi : 0.3316073417663574 time to delete rle : 0.0005738735198974609 batch 1 Loaded 27 chid ids of type : 3594 +++++++++++++++++++++Number RLEs to save : 3581 TO DO : save crop sub photo not yet done ! save time : 0.2889397144317627 nb_obj : 8 nb_hashtags : 3 time to prepare the origin masks : 0.098541259765625 time for calcul the mask position with numpy : 0.016033172607421875 nb_pixel_total : 1527087 time to create 1 rle with new method : 0.03259539604187012 time for calcul the mask position with numpy : 0.008770942687988281 nb_pixel_total : 478657 time to create 1 rle with new method : 0.0285799503326416 time for calcul the mask position with numpy : 0.005979061126708984 nb_pixel_total : 2190 time to create 1 rle with old method : 0.0025832653045654297 time for calcul the mask position with numpy : 0.006276130676269531 nb_pixel_total : 43879 time to create 1 rle with old method : 0.050573110580444336 time for calcul the mask position with numpy : 0.006056308746337891 nb_pixel_total : 3810 time to create 1 rle with old method : 0.004342317581176758 time for calcul the mask position with numpy : 0.006428956985473633 nb_pixel_total : 3377 time to create 1 rle with old method : 0.0040967464447021484 time for calcul the mask position with numpy : 0.007017374038696289 nb_pixel_total : 4117 time to create 1 rle with old method : 0.005095005035400391 time for calcul the mask position with numpy : 0.006247758865356445 nb_pixel_total : 6354 time to create 1 rle with old method : 0.007962226867675781 time for calcul the mask position with numpy : 0.0067479610443115234 nb_pixel_total : 4129 time to create 1 rle with old method : 0.005505800247192383 create new chi : 0.2117900848388672 time to delete rle : 0.0008871555328369141 batch 1 Loaded 17 chid ids of type : 3594 ++++++++++++++Number RLEs to save : 5320 TO DO : save crop sub photo not yet done ! save time : 0.3874356746673584 nb_obj : 9 nb_hashtags : 2 time to prepare the origin masks : 0.3677089214324951 time for calcul the mask position with numpy : 0.12288999557495117 nb_pixel_total : 1944703 time to create 1 rle with new method : 0.18204355239868164 time for calcul the mask position with numpy : 0.006405353546142578 nb_pixel_total : 3941 time to create 1 rle with old method : 0.0042798519134521484 time for calcul the mask position with numpy : 0.010116338729858398 nb_pixel_total : 6323 time to create 1 rle with old method : 0.007005453109741211 time for calcul the mask position with numpy : 0.009937763214111328 nb_pixel_total : 6512 time to create 1 rle with old method : 0.007277011871337891 time for calcul the mask position with numpy : 0.007670164108276367 nb_pixel_total : 2389 time to create 1 rle with old method : 0.002714872360229492 time for calcul the mask position with numpy : 0.006163120269775391 nb_pixel_total : 12639 time to create 1 rle with old method : 0.013768434524536133 time for calcul the mask position with numpy : 0.006189823150634766 nb_pixel_total : 7838 time to create 1 rle with old method : 0.008523225784301758 time for calcul the mask position with numpy : 0.006659746170043945 nb_pixel_total : 66697 time to create 1 rle with old method : 0.07211446762084961 time for calcul the mask position with numpy : 0.006188392639160156 nb_pixel_total : 2342 time to create 1 rle with old method : 0.0025942325592041016 time for calcul the mask position with numpy : 0.006483554840087891 nb_pixel_total : 20216 time to create 1 rle with old method : 0.021948575973510742 create new chi : 0.5207977294921875 time to delete rle : 0.0005037784576416016 batch 1 Loaded 19 chid ids of type : 3594 +++++++++++Number RLEs to save : 3133 TO DO : save crop sub photo not yet done ! save time : 0.2599005699157715 nb_obj : 9 nb_hashtags : 4 time to prepare the origin masks : 0.2650299072265625 time for calcul the mask position with numpy : 0.2263479232788086 nb_pixel_total : 1908671 time to create 1 rle with new method : 0.11239147186279297 time for calcul the mask position with numpy : 0.006239175796508789 nb_pixel_total : 2509 time to create 1 rle with old method : 0.0028204917907714844 time for calcul the mask position with numpy : 0.0062906742095947266 nb_pixel_total : 559 time to create 1 rle with old method : 0.0006914138793945312 time for calcul the mask position with numpy : 0.006212711334228516 nb_pixel_total : 76093 time to create 1 rle with old method : 0.08304166793823242 time for calcul the mask position with numpy : 0.006056785583496094 nb_pixel_total : 5876 time to create 1 rle with old method : 0.006483316421508789 time for calcul the mask position with numpy : 0.006003856658935547 nb_pixel_total : 1409 time to create 1 rle with old method : 0.0015177726745605469 time for calcul the mask position with numpy : 0.0059168338775634766 nb_pixel_total : 2897 time to create 1 rle with old method : 0.0032482147216796875 time for calcul the mask position with numpy : 0.006311893463134766 nb_pixel_total : 55293 time to create 1 rle with old method : 0.06168770790100098 time for calcul the mask position with numpy : 0.006295680999755859 nb_pixel_total : 12148 time to create 1 rle with old method : 0.013057947158813477 time for calcul the mask position with numpy : 0.0059778690338134766 nb_pixel_total : 8145 time to create 1 rle with old method : 0.008898735046386719 create new chi : 0.5853233337402344 time to delete rle : 0.0006988048553466797 batch 1 Loaded 19 chid ids of type : 3594 ++++++++++++++Number RLEs to save : 4445 TO DO : save crop sub photo not yet done ! save time : 0.32480597496032715 nb_obj : 9 nb_hashtags : 2 time to prepare the origin masks : 0.11405611038208008 time for calcul the mask position with numpy : 0.019752025604248047 nb_pixel_total : 1987479 time to create 1 rle with new method : 0.13943958282470703 time for calcul the mask position with numpy : 0.00646662712097168 nb_pixel_total : 30651 time to create 1 rle with old method : 0.032888174057006836 time for calcul the mask position with numpy : 0.005754947662353516 nb_pixel_total : 1954 time to create 1 rle with old method : 0.0022280216217041016 time for calcul the mask position with numpy : 0.005859375 nb_pixel_total : 6883 time to create 1 rle with old method : 0.007426261901855469 time for calcul the mask position with numpy : 0.006053924560546875 nb_pixel_total : 18989 time to create 1 rle with old method : 0.020521163940429688 time for calcul the mask position with numpy : 0.006135463714599609 nb_pixel_total : 2615 time to create 1 rle with old method : 0.0032334327697753906 time for calcul the mask position with numpy : 0.006386756896972656 nb_pixel_total : 4143 time to create 1 rle with old method : 0.004600048065185547 time for calcul the mask position with numpy : 0.006118297576904297 nb_pixel_total : 3288 time to create 1 rle with old method : 0.0037844181060791016 time for calcul the mask position with numpy : 0.006163358688354492 nb_pixel_total : 5329 time to create 1 rle with old method : 0.0058650970458984375 time for calcul the mask position with numpy : 0.0061643123626708984 nb_pixel_total : 12269 time to create 1 rle with old method : 0.013573884963989258 create new chi : 0.31510257720947266 time to delete rle : 0.0005342960357666016 batch 1 Loaded 19 chid ids of type : 3594 +++++++++Number RLEs to save : 3070 TO DO : save crop sub photo not yet done ! save time : 0.260545015335083 map_output_result : {1407288611: (0.0, 'Should be the crop_list due to order', 0), 1407287658: (0.0, 'Should be the crop_list due to order', 0), 1407287630: (0.0, 'Should be the crop_list due to order', 0), 1407287600: (0.0, 'Should be the crop_list due to order', 0), 1407287381: (0.0, 'Should be the crop_list due to order', 0), 1407287380: (0.0, 'Should be the crop_list due to order', 0), 1407287379: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1407288611, 1407287658, 1407287630, 1407287600, 1407287381, 1407287380, 1407287379] Looping around the photos to save general results len do output : 7 /1407288611.Didn't retrieve data . /1407287658.Didn't retrieve data . /1407287630.Didn't retrieve data . /1407287600.Didn't retrieve data . /1407287381.Didn't retrieve data . /1407287380.Didn't retrieve data . /1407287379.Didn't retrieve data . before output type Used above Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407288611', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287658', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287630', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287600', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287381', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287380', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287379', None, None, None, None, None, '4341986') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.015334129333496094 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.065051794052124 time spend to save output : 0.01567673683166504 total time spend for step 3 : 9.080728530883789 step4:ventilate_hashtags_in_portfolio Mon Feb 9 14:11:57 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 30348404 get user id for portfolio 30348404 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30348404 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','background','mal_croppe','pehd','pet_clair','autre','environnement','flou','metal','papier','pet_fonce')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30348404 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','background','mal_croppe','pehd','pet_clair','autre','environnement','flou','metal','papier','pet_fonce')) AND mptpi.`min_score`=0.5 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30348404 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','background','mal_croppe','pehd','pet_clair','autre','environnement','flou','metal','papier','pet_fonce')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/30348692,30348693,30348694,30348695,30348696,30348697,30348698,30348699,30348700,30348701,30348702?tags=carton,background,mal_croppe,pehd,pet_clair,autre,environnement,flou,metal,papier,pet_fonce Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1407288611, 1407287658, 1407287630, 1407287600, 1407287381, 1407287380, 1407287379] Looping around the photos to save general results len do output : 1 /30348404. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407288611', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287658', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287630', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287600', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287381', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287380', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287379', None, None, None, None, None, '4341986') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.018976688385009766 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.8972904682159424 time spend to save output : 0.019210100173950195 total time spend for step 4 : 0.9165005683898926 step5:final Mon Feb 9 14:11:58 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1407288611: ('0.12620370370370373',), 1407287658: ('0.12620370370370373',), 1407287630: ('0.12620370370370373',), 1407287600: ('0.12620370370370373',), 1407287381: ('0.12620370370370373',), 1407287380: ('0.12620370370370373',), 1407287379: ('0.12620370370370373',)} new output for save of step final : {1407288611: ('0.12620370370370373',), 1407287658: ('0.12620370370370373',), 1407287630: ('0.12620370370370373',), 1407287600: ('0.12620370370370373',), 1407287381: ('0.12620370370370373',), 1407287380: ('0.12620370370370373',), 1407287379: ('0.12620370370370373',)} [1407288611, 1407287658, 1407287630, 1407287600, 1407287381, 1407287380, 1407287379] Looping around the photos to save general results len do output : 7 /1407288611.Didn't retrieve data . /1407287658.Didn't retrieve data . /1407287630.Didn't retrieve data . /1407287600.Didn't retrieve data . /1407287381.Didn't retrieve data . /1407287380.Didn't retrieve data . /1407287379.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407288611', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287658', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287630', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287600', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287381', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287380', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287379', None, None, None, None, None, '4341986') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.014394521713256836 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.1380939483642578 time spend to save output : 0.014802217483520508 total time spend for step 5 : 0.15289616584777832 step6:blur_detection Mon Feb 9 14:11:58 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1.jpg resize: (1080, 1920) 1407288611 -3.955242510335981 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5.jpg resize: (1080, 1920) 1407287658 -4.2085537536297775 treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362.jpg resize: (1080, 1920) 1407287630 -4.218190468178188 treat image : temp/1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a.jpg resize: (1080, 1920) 1407287600 -4.266876314886276 treat image : temp/1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502.jpg resize: (1080, 1920) 1407287381 -2.919613276633804 treat image : temp/1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1.jpg resize: (1080, 1920) 1407287380 -3.345257109440685 treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d.jpg resize: (1080, 1920) 1407287379 -3.655704799559112 treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847174_0.png resize: (185, 238) 1407310832 -3.5665283294902785 treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847180_0.png resize: (86, 105) 1407310834 -3.731802657451075 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847182_0.png resize: (94, 114) 1407310835 -4.136088370628175 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847189_0.png resize: (186, 221) 1407310836 -3.499363029192034 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847191_0.png resize: (59, 82) 1407310838 0.42191263930434464 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847193_0.png resize: (60, 83) 1407310839 -1.6678894999532097 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847195_0.png resize: (61, 114) 1407310840 -2.7361620511191362 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847197_0.png resize: (81, 130) 1407310842 -5.191660758206426 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847198_0.png resize: (131, 78) 1407310843 -3.4711311781727923 treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847201_0.png resize: (100, 124) 1407310844 -2.205002270551755 treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847203_0.png resize: (87, 149) 1407310846 -1.6121254161619318 treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847205_0.png resize: (99, 98) 1407310847 -4.712428441377317 treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847206_0.png resize: (72, 144) 1407310848 -1.5548952234471063 treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847208_0.png resize: (156, 216) 1407310850 -2.3283485824039425 treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847209_0.png resize: (144, 93) 1407310851 -2.845502327562027 treat image : temp/1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847212_0.png resize: (72, 87) 1407310853 -2.869635042540174 treat image : temp/1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847213_0.png resize: (114, 88) 1407310854 -1.8684666032480863 treat image : temp/1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847214_0.png resize: (55, 84) 1407310855 2.925746261628608 treat image : temp/1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847220_0.png resize: (229, 209) 1407310857 -3.1994162927172694 treat image : temp/1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847221_0.png resize: (70, 50) 1407310858 -1.0349898768187769 treat image : temp/1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847222_0.png resize: (225, 535) 1407310859 -4.544824445032022 treat image : temp/1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847223_0.png resize: (81, 151) 1407310861 -2.3272243588653576 treat image : temp/1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847224_0.png resize: (117, 170) 1407310862 -3.117767875691434 treat image : temp/1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502_rle_crop_4124847225_0.png resize: (65, 60) 1407310863 -2.916680756698534 treat image : temp/1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847232_0.png resize: (78, 57) 1407310865 -1.8960630540925945 treat image : temp/1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847235_0.png resize: (457, 308) 1407310866 -3.0160181860575297 treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847240_0.png resize: (73, 77) 1407310867 -1.917172222101959 treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847242_0.png resize: (56, 65) 1407310869 -1.0804581257963417 treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847245_0.png resize: (41, 59) 1407310870 3.2945499781396195 treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847246_0.png resize: (201, 224) 1407310871 -3.605266895726709 treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847176_0.png resize: (81, 119) 1407311022 -4.076828178366121 treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847181_0.png resize: (111, 163) 1407311023 -5.548426454135874 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847183_0.png resize: (71, 102) 1407311025 -2.363032331797357 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847184_0.png resize: (151, 188) 1407311027 -5.093562389804884 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847185_0.png resize: (38, 42) 1407311028 -2.574979072533873 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847186_0.png resize: (49, 96) 1407311030 -3.8260495367104195 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847187_0.png resize: (100, 105) 1407311032 -4.392850709270957 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847188_0.png resize: (106, 115) 1407311034 -4.8871608673925495 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847192_0.png resize: (63, 66) 1407311036 -4.308157636925881 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847194_0.png resize: (49, 77) 1407311037 -5.0743094402984354 treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847196_0.png resize: (59, 93) 1407311039 -3.316100144948577 treat image : 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temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847177_0.png resize: (67, 61) 1407311155 -4.6478613105092546 treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847207_0.png resize: (50, 74) 1407311156 -3.407481754985286 Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 80 time used for this insertion : 0.018255233764648438 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 80 time used for this insertion : 0.02154827117919922 save missing photos in datou_result : time spend for datou_step_exec : 7.282277822494507 time spend to save output : 0.04560375213623047 total time spend for step 6 : 7.327881574630737 step7:brightness Mon Feb 9 14:12:06 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1.jpg treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5.jpg treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362.jpg treat image : temp/1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a.jpg treat image : temp/1770642628_475589_1407287381_f0bee4441ad0195899d40cfe1e2e6502.jpg treat image : temp/1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1.jpg treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d.jpg treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847174_0.png treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847180_0.png treat image : 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temp/1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847236_0.png treat image : temp/1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847237_0.png treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847238_0.png treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847239_0.png treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847241_0.png treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847243_0.png treat image : temp/1770642628_475589_1407287379_852da211556c00cd1bbc5759da5ad91d_rle_crop_4124847244_0.png treat image : temp/1770642628_475589_1407287658_407364b77085299815c7352c994fddb5_rle_crop_4124847190_0.png treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847202_0.png treat image : temp/1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847234_0.png treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847178_0.png treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847179_0.png treat image : temp/1770642628_475589_1407287600_d335573b973011aa96616bf3418cf65a_rle_crop_4124847219_0.png treat image : temp/1770642628_475589_1407287380_fbef0111205a5202537630d442c103d1_rle_crop_4124847231_0.png treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847175_0.png treat image : temp/1770642628_475589_1407288611_43439ed0b97010537bdb1b608ced14f1_rle_crop_4124847177_0.png treat image : temp/1770642628_475589_1407287630_931c2d9f5e2c8413524e6a835b423362_rle_crop_4124847207_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 80 time used for this insertion : 0.028094768524169922 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 80 time used for this insertion : 0.02263927459716797 save missing photos in datou_result : time spend for datou_step_exec : 2.1488194465637207 time spend to save output : 0.05627322196960449 total time spend for step 7 : 2.205092668533325 step8:velours_tree Mon Feb 9 14:12:08 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.23480606079101562 time spend to save output : 4.172325134277344e-05 total time spend for step 8 : 0.2348477840423584 step9:send_mail_cod Mon Feb 9 14:12:08 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P30348404_09-02-2026_14_12_08.pdf 30348692 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette303486921770642728 30348693 imagette303486931770642730 30348694 imagette303486941770642730 30348695 imagette303486951770642730 30348696 change filename to text .change filename to text .change filename to text .change filename to text .imagette303486961770642730 30348697 change filename to text .change filename to text .change filename to text .imagette303486971770642730 30348699 imagette303486991770642730 30348700 change filename to text .change filename to text .change filename to text .imagette303487001770642730 30348701 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette303487011770642731 30348702 imagette303487021770642732 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=30348404 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/30348692,30348693,30348694,30348695,30348696,30348697,30348698,30348699,30348700,30348701,30348702?tags=carton,background,mal_croppe,pehd,pet_clair,autre,environnement,flou,metal,papier,pet_fonce your option no_mail is active, we will not send the real mail to your client args[1407288611] : ((1407288611, -3.955242510335981, 492609224), (1407288611, -0.669250980699769, 501862349), '0.12620370370370373') We are sending mail with results at report@fotonower.com args[1407287658] : ((1407287658, -4.2085537536297775, 492609224), (1407287658, -0.20605252018382897, 496442774), '0.12620370370370373') We are sending mail with results at report@fotonower.com args[1407287630] : ((1407287630, -4.218190468178188, 492609224), (1407287630, -0.13542101407985796, 496442774), '0.12620370370370373') We are sending mail with results at report@fotonower.com args[1407287600] : ((1407287600, -4.266876314886276, 492609224), (1407287600, -0.18366713547025046, 496442774), '0.12620370370370373') We are sending mail with results at report@fotonower.com args[1407287381] : ((1407287381, -2.919613276633804, 492609224), (1407287381, -0.21886346753867714, 496442774), '0.12620370370370373') We are sending mail with results at report@fotonower.com args[1407287380] : ((1407287380, -3.345257109440685, 492609224), (1407287380, -0.16816305532425604, 496442774), '0.12620370370370373') We are sending mail with results at report@fotonower.com args[1407287379] : ((1407287379, -3.655704799559112, 492609224), (1407287379, -0.1897836627956786, 496442774), '0.12620370370370373') We are sending mail with results at report@fotonower.com refus_total : 0.12620370370370373 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=30348404 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30348404_09-02-2026_14_12_08.pdf results_Auto_P30348404_09-02-2026_14_12_08.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30348404_09-02-2026_14_12_08.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3318','30348404','results_Auto_P30348404_09-02-2026_14_12_08.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30348404_09-02-2026_14_12_08.pdf','pdf','','0.35','0.12620370370370373') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1407288611, 1407287658, 1407287630, 1407287600, 1407287381, 1407287380, 1407287379] Looping around the photos to save general results len do output : 0 before output type Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407288611', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287658', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287630', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287600', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287381', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287380', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287379', None, None, None, None, None, '4341986') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 7 time used for this insertion : 0.018157005310058594 save_final save missing photos in datou_result : time spend for datou_step_exec : 5.378971815109253 time spend to save output : 0.018335819244384766 total time spend for step 9 : 5.397307634353638 step10:split_time_score Mon Feb 9 14:12:14 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('10', 7),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 09022026 30348404 Nombre de photos uploadées : 7 / 23040 (0%) 09022026 30348404 Nombre de photos taguées (types de déchets): 0 / 7 (0%) 09022026 30348404 Nombre de photos taguées (volume) : 0 / 7 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 4.76837158203125e-06 ??????? elapsed_time : fill_and_build_computed_from_old_data 0.0003936290740966797 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.2398524284362793 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.052971634383078774 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30347165_09-02-2026_10_08_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30347165 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30347165 AND mptpi.`type`=3726 To do Qualite : 0.14781100206485906 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30346641_09-02-2026_08_41_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30346641 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30346641 AND mptpi.`type`=3726 To do Qualite : 0.1310208459079257 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30346388_09-02-2026_07_33_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30346388 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30346388 AND mptpi.`type`=3726 To do Qualite : 0.049195934997736 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30346646_09-02-2026_08_35_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30346646 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30346646 AND mptpi.`type`=3726 To do Qualite : 0.10421340116204908 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30346816_09-02-2026_09_05_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30346816 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30346816 AND mptpi.`type`=3726 To do Qualite : 0.1181417534772471 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30346818_09-02-2026_09_09_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30346818 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30346818 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30348399 order by id desc limit 1 Qualite : 0.0386272592484278 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30348401_09-02-2026_14_10_50.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30348401 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30348401 AND mptpi.`type`=3726 To do Qualite : 0.12620370370370373 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30348404_09-02-2026_14_12_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30348404 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30348404 AND mptpi.`type`=3594 To do Qualite : 0.11563490767736301 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30348408_09-02-2026_14_08_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30348408 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30348408 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'09022026': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1407288611, 1407287658, 1407287630, 1407287600, 1407287381, 1407287380, 1407287379] Looping around the photos to save general results len do output : 1 /30348404Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407288611', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287658', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287630', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287600', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287381', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287380', None, None, None, None, None, '4341986') ('3318', None, None, None, None, None, None, None, '4341986') ('3318', '30348404', '1407287379', None, None, None, None, None, '4341986') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.017946720123291016 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.3348357677459717 time spend to save output : 0.018163204193115234 total time spend for step 10 : 2.352998971939087 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 7 set_done_treatment 38.73user 22.63system 1:50.47elapsed 55%CPU (0avgtext+0avgdata 2820064maxresident)k 1401880inputs+35528outputs (10068major+1993406minor)pagefaults 0swaps